iperov / litenn

Lightweight machine learning library based on OpenCL 1.2

LiteNN lightweight Machine Learning library based on OpenCL 1.2 and written in pure python
suitable for most popular ML tasks such as regression, recognition, classification, autoencoders, GANs.
Features
written in pure python	Nothing to build from source! No headache with cmake, bazel, compilers, environments, etc.
future-proof	unlike CUDA, OpenCL 1.2 does not break backward compatibility with new video cards, so your app will work on future devices.
Simplified and pytorch-like	PyTorch-like, but more lightweight architecture with simplified things.
Easy to experiment	Implement your own custom GPU-accelerated ops much more faster, using OpenCL C-language as text directly in python. You don't need to compile or build from source.
user namespace	litenn is namespace for users. You will not see internal classes or functions in your vscode intellisense hint. All things in litenn namespace are ready to use, contain editor hint, and the source code can be directly explored from your IDE.
Minimal dependencies	numpy only
Getting started
pip install litenn import litenn as nn nn.test.all()
User guide Developer guide LiteNN-apps
#machinelearning #machine-learning #deep-learning #deeplearning #deep-neural-networks #neural-networks #neural-nets #opencl